移动通信网络产生的通话详单记录（CDR）被广泛用于人类移动性研究，但单一移动运营商提供的CDR数据本质上存在偏差，因为其用户群体无法准确反映总体人口分布。基于智利一家主要运营商在圣地亚哥市的数据，我们发现其用户基础在社会经济地位上存在明显偏斜，导致半径回转率（radius of gyration）等聚合指标因实际观测人群的构成而失真。为校正此类抽样偏差，我们应用了多层次回归与事后分层法（MRP）——该方法目前尚未成为基于CDR的移动性研究中的标准做法。我们构建了一个贝叶斯多层次模型，以个体移动性为因变量，以社会经济地位、性别和地理区位为协变量，并在市级行政区（comuna）层面实施部分池化（partial pooling）；随后将模型预测结果按人口普查的人口统计结构进行事后分层。该方法使基于CDR的平均半径回转率原始估计值降低了约17%。值得注意的是，仅使用地理信息的简化版模型仍能捕捉大部分偏差，表明只要社会经济群体在空间上存在分布规律，即使用户的社会经济构成未被完全掌握，MRP依然具有实用价值。本例展示了MRP如何为非代表性CDR衍生移动性估计提供一种有原则的校正方法，而非将运营商样本简单视作随机总体样本。

Call detail records (CDR) from mobile phone networks are widely used to study human mobility however CDR data from a single mobile operator are inherently biased because the observed users do not mirror the population distribution. Using data from a major Chilean carrier in Santiago, we observe the user base is skewed by socioeconomic group, so aggregate metrics like radius of gyration are distorted by the population that is actually observed. To correct this sampling bias, we apply multilevel regression and poststratification (MRP), a method that is not yet standard for CDR-based mobility studies. We fit a Bayesian multilevel model for individual mobility using socioeconomic status, gender, and geography, with partial pooling across comunas, and then poststratify the predictions to match census demographics. This approach reduces the naive CDR estimate of average radius of gyration by about 17%. Importantly, a version of the model that uses only geographic information still captures much of the bias, showing that MRP can be useful even when the socioeconomic composition of users is not fully known, as long as spatial patterns of socioeconomic groups exist. This example demonstrates how MRP can provide a principled correction for non-representative CDR-derived mobility estimates, rather than treating the carrier sample as if it were a random population sample.